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What is Data Detection and Response (DDR)?

Craig Smilowitz | 8 min read| Published On - June 6, 2024

Data Detection and Response

The rapid growth of data has created both opportunities and risks. With the increasing frequency of cyberattacks and data breaches, organizations face significant financial, regulatory, and reputational consequences.

Despite the numerous security products available, data remains vulnerable to leaks and unauthorized sharing. The issue lies in the fragmented nature of data security, which is spread across various products and systems.

To address this, data security must adapt to the modern IT landscape, where data flows are porous and unpredictable. A new approach is needed, focusing on proactive, data-centric protection that can detect and respond to threats in real-time, protect compromised data, and minimize the effects of breaches.

What is Data Detection and Response (DDR)?

Data Detection and Response (DDR) takes a proactive approach to data protection by focusing on the data itself, rather than just network and infrastructure security. With DDR, real-time monitoring and analysis of data enable instant detection, alerting, and response to active threats. By tracking data movement, access, and use across all endpoints, DDR solutions follow the data’s “lineage” to stop exfiltration in real-time.

This approach offers several key advantages, including data-centric visibility, allowing for tracking of sensitive data regardless of its location, early detection of anomalies that may indicate a breach, addressing insider risk by identifying unusual activity from trusted users, and enhanced compliance with regulations like GDPR by demonstrating due diligence in handling sensitive information.

Why Do I Need Data Detection and Response (DDR)?

The growing threat of data breaches has reached alarming levels, with the average cost of a breach reaching an all-time high of $9.48 million. As data is increasingly scattered across the cloud, mobile devices, and SaaS platforms, traditional security measures are no longer sufficient to protect against data breaches.

In today’s complex and decentralized IT landscape, even authorized users can pose a risk, and data breaches can have devastating consequences. To effectively address these threats, a new approach is needed.

DDR is a game-changing solution that identifies and responds to threats at the data level, offering a comprehensive and adaptive defense. By leveraging advanced analytics and machine learning, DDR provides instant visibility into data stores, real-time protection, and response capabilities, revolutionizing the way we approach cybersecurity.

Why Data Detection and Response (DDR) is Important

Below are 6 key reasons why DDR is crucial for your business:

Comprehensive data monitoring

Monitor data across multi-cloud environments and cloud-based software as a service (SaaS) applications by monitoring activity logs. This ensures that data is visible and accessible regardless of where it resides or moves.

Advanced threat detection

Detect data threats that other solutions may not, by monitoring data at its source. This eliminates blind spots and allows for the detection of threats that involve malicious actors accessing data through authorized accounts.

Real-time incident response

Stop data exfiltration as soon as it occurs, using real-time or near real-time anomaly detection to identify unusual data access patterns. Trigger alerts to notify relevant teams to block or isolate affected systems, minimizing the risk of data breaches.

Enhanced breach investigations

Provide data context, such as what data has been compromised, where it is located, and what entities have accessed the data, to help security teams assess risk, understand the blast radius, and determine next steps. This enables a more effective and efficient response to data breaches.

Efficient alert management

Minimize costs and reduce alert fatigue by offering risk-based prioritization and alert customization. This ensures that responding teams are focused on the most critical threats, and that alerts are tailored to the specific needs of each organization.

Compliance and risk management

Lessen the risk of data regulation violations by flagging violations as soon as they happen. This helps organizations comply with data regulation frameworks and avoid legal consequences and fines. With this solution, organizations can proactively identify and address potential issues before they escalate.

What To Look For In a DDR Solution

A DDR solution is essential to help you accurately identify and classify sensitive data, detect potential threats in real-time, and ensure compliance with regulatory requirements.

Data classification

When evaluating a DDR solution, it’s essential to prioritize data classification, which involves identifying and tagging sensitive data. This ensures that critical data is properly protected and monitored. It’s important to consider “data lineage”, which includes factors such as origin, storage location, access history, and intended use to provide a comprehensive understanding of data sensitivity.

Continuous monitoring

This involves tracking data usage and movement in real-time, enabling swift detection and response to potential threats. Advanced analytics and machine learning play a key role in detecting subtle patterns indicative of a threat. Automated response mechanisms, such as quarantining files, blocking accounts, or limiting permissions, can contain potential threats and prevent damage to your organization’s data.

Compliance alignment

When evaluating a DDR solution, it’s essential to consider compliance alignment with regulations such as GDPR. This involves mapping DDR activity, including data audits and access logs, to the requirements of these regulations. By demonstrating a proactive approach to data privacy, you can ensure that your organization is in compliance withttps://www.lepide.com/use-casesh regulatory requirements and maintains a strong reputation for data security.

How Does Data Detection & Response (DDR) Work?

DDR solutions consist of four key components: Monitoring, Detection, Alert, and Response. Each component plays a crucial role in detecting and responding to potential data breaches.

Monitoring

Monitoring is the initial step in the DDR process, involving continuous scanning of data through activity logs such as AWS CloudTrail and Azure Monitor. This component can focus on assets with sensitive data to keep costs low and optimize scanning by orchestrating activity log monitoring. By monitoring data activity, DDR solutions can identify potential threats and anomalies in real-time.

Detection

Detection is the next step in the DDR process, where anomalous data access and suspicious behavior are identified. This is achieved through behavior analytics, machine learning, and ongoing research on cyber-attack methods. DDR solutions may detect anomalies such as:

  • Data accessed from an unusual geo-location or IP address
  • logging system deactivated for an asset with sensitive data
  • An abnormally large batch of sensitive data was downloaded, deleted, or modified
  • An external entity downloaded sensitive data
  • An anonymous entity accessed sensitive data for the first time

Alerting

When a threat is detected, an alert is triggered. This notifies the right teams of the potential data breach, allowing them to take swift action. The best DDR tools use data classification to identify sensitive data and send alerts only for threats to assets with sensitive data. This reduces noise for security operations center (SOC) teams and prevents alert fatigue.

Response

The Response component is the final step in the DDR process, where automated actions are taken to contain and mitigate detected threats. This minimizes the potential impact and thwarts further damage. DDR solutions often integrate with security management systems like:

  • ITSM (IT service management)
  • SIEM (security information and event management)
  • SOAR (security orchestration, automation, and response)

What Is The Difference Between DDR, EDR, and XDR?

DDR, EDR, and XDR differ in their focus, with EDR and XDR primarily concentrating on threat detection and response at the endpoint level.

In contrast, DDR takes a more comprehensive approach by monitoring data across a wide-range of platforms, including both on-premise and cloud-based environments.

From a deployment and maintenance perspective, DDR stands out from EDR and XDR in that it is often agentless, whereas the latter two require agents to be installed on endpoints. This agentless approach offers ease of deployment and maintenance, with minimal impact on performance.

In contrast, agent-based solutions require ongoing management of additional software agents on endpoints, which can impact performance and necessitate regular maintenance.

How Lepide Helps with Data Detection and Response

Lepide Data Security Platform is a purpose-built, data-centric, data detection and response solution. We’re helping our customers implement stringent data detection and response throughout their on-premises, cloud, or hybrid environments. We do this in a few key ways:

  • Data discovery and classification: We give organizations visibility over where their sensitive data is located across cloud and on-premises data stores. This includes places like Windows File Server, Microsoft 365, and more. By understanding what sensitive data you have, and where it is, you can begin the security process.
  • User activity monitoring: Lepide learns what the normal behavior of your users looks like in terms of how they are interacting with your sensitive data. Its anomaly-spotting technology enables you to detect the signs of breaches or unauthorized access attempts.
  • Alerting and reporting: Lepide can generate real-time alerts and execute automated threat response actions off the back of unusual, unwanted, or suspicious events or patterns. This enables you to quickly and effectively respond to potential incidents and mitigate damages.

If you’d like to see more about Lepide, schedule a demo with one of our engineers or download a free trial.

Conclusion

To conclude, it should be noted that a truly successful data security strategy lies not solely in the technology itself, but also in its harmonious integration with existing security processes and incident response plans.

Before investing in data discovery and reporting tools, it’s crucial to assess the maturity of your overall processes. This includes having robust incident response planning, well-defined data governance, and adequately trained employees aware of the threats to sensitive business data.

By taking a multi-faceted approach that considers technology, processes, and people, we can ensure a more effective and sustainable future for data protection.

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